




'''simple docstring'''





from ...configuration_utils import PretrainedConfig
from ...utils import logging


_SCREAMING_SNAKE_CASE				:  List[Any] 						=					logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE				:  List[str] 						=					{
    "EleutherAI/gpt-neox-20b": "https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/config.json",
    # See all GPTNeoX models at https://huggingface.co/models?filter=gpt_neox
}




class 					_snake_case						(      _UpperCAmelCase				):
			lowerCAmelCase_	: List[Any]					=       "gpt_neox"




			def __init__(   self							,				a__=50_432							,				a__=6_144							,				a__=44							,				a__=64							,				a__=24_576							,				a__="gelu"							,				a__=0.2_5							,				a__=10_000							,				a__=0.0							,				a__=0.0							,				a__=0.1							,				a__=2_048							,				a__=0.0_2							,				a__=1e-5							,				a__=True							,				a__=0							,				a__=2							,				a__=False							,				a__=True							,				a__=None							,				**a__							,				)							->							List[Any]:

										'''simple docstring'''




										super().__init__(bos_token_id=a__							,				eos_token_id=a__							,				**a__				)
										snake_case_         = vocab_size
										snake_case_         = max_position_embeddings
										snake_case_         = hidden_size
										snake_case_         = num_hidden_layers
										snake_case_         = num_attention_heads
										snake_case_         = intermediate_size
										snake_case_         = hidden_act
										snake_case_         = rotary_pct
										snake_case_         = rotary_emb_base
										snake_case_         = attention_dropout
										snake_case_         = hidden_dropout
										snake_case_         = classifier_dropout
										snake_case_         = initializer_range
										snake_case_         = layer_norm_eps
										snake_case_         = use_cache
										snake_case_         = tie_word_embeddings
										snake_case_         = use_parallel_residual
										snake_case_         = rope_scaling
										self._rope_scaling_validation()

										if self.hidden_size % self.num_attention_heads != 0:
																	raise ValueError(
																	    "The hidden size is not divisble by the number of attention heads! Make sure to update them!"				)




			def   lowerCAmelCase__							(   self				)							->							Optional[int]:

										'''simple docstring'''




										if self.rope_scaling is None:
																	return

										if not isinstance(self.rope_scaling							,				a__				) or len(self.rope_scaling				) != 2:
																	raise ValueError(
																	    "`rope_scaling` must be a dictionary with with two fields, `name` and `factor`, "
																	    F'got {self.rope_scaling}'				)
										snake_case_         = self.rope_scaling.get("type"							,				a__				)
										snake_case_         = self.rope_scaling.get("factor"							,				a__				)
										if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
																	raise ValueError(
																	    F'`rope_scaling`\'s name field must be one of [\'linear\', \'dynamic\'], got {rope_scaling_type}'				)
										if rope_scaling_factor is None or not isinstance(a__							,				a__				) or rope_scaling_factor <= 1.0:
																	raise ValueError(F'`rope_scaling`\'s factor field must be an float > 1, got {rope_scaling_factor}'				)











"""simple docstring"""





import os
import shutil
from pathlib import Path
from typing import Optional, Union

import numpy as np
from huggingface_hub import hf_hub_download

from ..utils import ONNX_EXTERNAL_WEIGHTS_NAME, ONNX_WEIGHTS_NAME, is_onnx_available, logging


if is_onnx_available():
       import onnxruntime as ort


SCREAMING_SNAKE_CASE__   =  logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__   =  {
    "tensor(bool)": np.bool_,
    "tensor(int8)": np.inta,
    "tensor(uint8)": np.uinta,
    "tensor(int16)": np.intaa,
    "tensor(uint16)": np.uintaa,
    "tensor(int32)": np.intaa,
    "tensor(uint32)": np.uintaa,
    "tensor(int64)": np.intaa,
    "tensor(uint64)": np.uintaa,
    "tensor(float16)": np.floataa,
    "tensor(float)": np.floataa,
    "tensor(double)": np.floataa,
}

class 						lowercase   :





      def __init__(			self ,		lowercase=None ,		**lowercase					)						->	int:
            logger.info("""`diffusers.OnnxRuntimeModel` is experimental and might change in the future."""					)
            lowerCAmelCase    		=			model
            lowerCAmelCase    		=			kwargs.get("""model_save_dir""" ,		lowercase					)
            lowerCAmelCase    		=			kwargs.get("""latest_model_name""" ,		lowercase					)





      def __call__(			self ,		**lowercase					)						->	List[Any]:
            lowerCAmelCase    		=			{k: np.array(lowercase					) for k, v in kwargs.items()}
            return self.model.run(lowercase ,		lowercase					)





      @staticmethod
      def        _snake_case     (			lowercase ,		lowercase=None ,		lowercase=None					)						->	Optional[Any]:
            if provider is None:
                  logger.info("""No onnxruntime provider specified, using CPUExecutionProvider"""					)
                  lowerCAmelCase    		=			"""CPUExecutionProvider"""

            return ort.InferenceSession(lowercase ,		providers=[provider] ,		sess_options=lowercase					)





      def        _snake_case     (			self ,		lowercase ,		lowercase = None ,		**lowercase					)						->	str:
            lowerCAmelCase    		=			file_name if file_name is not None else ONNX_WEIGHTS_NAME

            lowerCAmelCase    		=			self.model_save_dir.joinpath(self.latest_model_name					)
            lowerCAmelCase    		=			Path(lowercase					).joinpath(lowercase					)
            try:
                  shutil.copyfile(lowercase ,		lowercase					)
            except shutil.SameFileError:
                  pass

            # copy external weights (for models >2GB)
            lowerCAmelCase    		=			self.model_save_dir.joinpath(lowercase					)
            if src_path.exists():
                  lowerCAmelCase    		=			Path(lowercase					).joinpath(lowercase					)
                  try:
                        shutil.copyfile(lowercase ,		lowercase					)
                  except shutil.SameFileError:
                        pass





      def        _snake_case     (			self ,		lowercase ,		**lowercase ,		)						->	str:
            if os.path.isfile(lowercase					):
                  logger.error(f'Provided path ({save_directory}) should be a directory, not a file'					)
                  return

            os.makedirs(lowercase ,		exist_ok=lowercase					)

            # saving model weights/files
            self._save_pretrained(lowercase ,		**lowercase					)





      @classmethod
      def        _snake_case     (			cls ,		lowercase ,		lowercase = None ,		lowercase = None ,		lowercase = False ,		lowercase = None ,		lowercase = None ,		lowercase = None ,		lowercase = None ,		**lowercase ,		)						->	Union[str, Any]:
            lowerCAmelCase    		=			file_name if file_name is not None else ONNX_WEIGHTS_NAME
            # load model from local directory
            if os.path.isdir(lowercase					):
                  lowerCAmelCase    		=			OnnxRuntimeModel.load_model(
                      os.path.join(lowercase ,		lowercase					) ,		provider=lowercase ,		sess_options=lowercase					)
                  lowerCAmelCase    		=			Path(lowercase					)
            # load model from hub
            else:
                  # download model
                  lowerCAmelCase    		=			hf_hub_download(
                      repo_id=lowercase ,		filename=lowercase ,		use_auth_token=lowercase ,		revision=lowercase ,		cache_dir=lowercase ,		force_download=lowercase ,		)
                  lowerCAmelCase    		=			Path(lowercase					).parent
                  lowerCAmelCase    		=			Path(lowercase					).name
                  lowerCAmelCase    		=			OnnxRuntimeModel.load_model(lowercase ,		provider=lowercase ,		sess_options=lowercase					)
            return cls(model=lowercase ,		**lowercase					)





      @classmethod
      def        _snake_case     (			cls ,		lowercase ,		lowercase = True ,		lowercase = None ,		lowercase = None ,		**lowercase ,		)						->	List[str]:
            lowerCAmelCase    		=			None
            if len(str(lowercase					).split("""@"""					)					) == 2:
                  lowerCAmelCase		,						lowerCAmelCase    		=			model_id.split("""@"""					)

            return cls._from_pretrained(
                model_id=lowercase ,		revision=lowercase ,		cache_dir=lowercase ,		force_download=lowercase ,		use_auth_token=lowercase ,		**lowercase ,		)






